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Feedzai Interview Questions

The questions to prepare for Feedzai interviews, across all roles. Questions from real interview reports rank first. Updated weekly.

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~7htotal time
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1
CodingStart here. 4 questions · ~35 min
O(1) Insert Delete Random SetEasy
Practice

Implement a set-like structure using an array and hash map to support insert, delete, and random access in average O(1) time.

Hash TablesArraysData StructuresFeedzai
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2
Execution4 questions · ~35 min
Handle Scope Changes Mid-DevelopmentMedium

Explain how you manage scope changes during development without losing delivery control, stakeholder alignment, or product quality.

AgileScope ManagementProject ManagementFeedzai
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3
Metrics4 questions · ~35 min
Choose Product Success KPIsEasy

Define a practical KPI set for product success, balancing a north star metric with leading indicators.

North Star MetricKPIsLeading IndicatorsFeedzai
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4
A/B Testing & Experimentation3 questions · ~26 min
Common Pitfalls in Experiment ResultsHard

Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.

PeekingNovelty EffectSample Ratio MismatchFeedzai
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5
SQL & Data Manipulation4 questions + 3 drills · ~65 min
Handling Missing and Dirty SQL DataMedium

Explain how to profile, clean, and standardize missing or dirty data before analysis.

Data WranglingCase WhenQualityFeedzai
Flag Suspicious Driver Signup ClustersHard
Practice
Practice drill

Use CTEs, joins, aggregations, and CASE logic to flag Lyft driver accounts with suspicious early activity patterns.

fraud detectionData AnalysisRisk ManagementLyft
SQL Rolling Average and RankingHard
Practice
Practice drill

Use CTEs and window functions to calculate 7-day transaction averages and rank Loft users within each region.

Window FunctionsRankingRunning TotalsLoftArcadisCgi Nederland
Find Fulfillment Bottlenecks from TimestampsHard
Practice
Practice drill

Use LAG and window functions to identify the slowest stage in the fulfillment chain.

Window FunctionsData WranglingRunning TotalsInstacart
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6
Machine Learning4 questions · ~35 min
Prevent Overfitting in ML ModelsEasy

Explain how to reduce overfitting using regularization, validation, and model selection.

Cross-ValidationBias-Variance TradeoffRegularizationFeedzai
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7
More topics27 questions · ~237 min
Gather and Prioritize User FeedbackEasy

How to collect user feedback and turn it into product improvements.

User ResearchUser NeedsPain PointsFeedzai
Understanding Type I and Type II Errors in TestingMedium

Differentiate between Type I and Type II errors in hypothesis testing with a practical example.

Hypothesis TestingStatistical SignificanceP-ValuesFeedzai
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The finish line: interview-readyComplete all 50 questions plus 3 hands-on drills to finish this plan.